16,710 research outputs found
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train
a shared model jointly without transmitting local raw data, has become a
prevalent recommendation paradigm with privacy-preserving advantages. However,
previous work on FRS performs similarity search via inner product in continuous
embedding space, which causes an efficiency bottleneck when the scale of items
is extremely large. We argue that such a scheme in federated settings ignores
the limited capacities in resource-constrained user devices (i.e., storage
space, computational overhead, and communication bandwidth), and makes it
harder to be deployed in large-scale recommender systems. Besides, it has been
shown that transmitting local gradients in real-valued form between server and
clients may leak users' private information. To this end, we propose a
lightweight federated recommendation framework with privacy-preserving matrix
factorization, LightFR, that is able to generate high-quality binary codes by
exploiting learning to hash technique under federated settings, and thus enjoys
both fast online inference and economic memory consumption. Moreover, we devise
an efficient federated discrete optimization algorithm to collaboratively train
model parameters between the server and clients, which can effectively prevent
real-valued gradient attacks from malicious parties. Through extensive
experiments on four real-world datasets, we show that our LightFR model
outperforms several state-of-the-art FRS methods in terms of recommendation
accuracy, inference efficiency and data privacy.Comment: Accepted by ACM Transactions on Information Systems (TOIS
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems
(FedRecs) have garnered increasing interest in the realm of on-device
recommender systems. However, most existing FedRecs only allow participating
clients to collaboratively train a recommendation model of the same public
parameter size. Training a model of the same size for all clients can lead to
suboptimal performance since clients possess varying resources. For example,
clients with limited training data may prefer to train a smaller recommendation
model to avoid excessive data consumption, while clients with sufficient data
would benefit from a larger model to achieve higher recommendation accuracy. To
address the above challenge, this paper introduces HeteFedRec, a novel FedRec
framework that enables the assignment of personalized model sizes to
participants. In HeteFedRec, we present a heterogeneous recommendation model
aggregation strategy, including a unified dual-task learning mechanism and a
dimensional decorrelation regularization, to allow knowledge aggregation among
recommender models of different sizes. Additionally, a relation-based ensemble
knowledge distillation method is proposed to effectively distil knowledge from
heterogeneous item embeddings. Extensive experiments conducted on three
real-world recommendation datasets demonstrate the effectiveness and efficiency
of HeteFedRec in training federated recommender systems under heterogeneous
settings
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
Preserving privacy and reducing communication costs for edge users pose
significant challenges in recommendation systems. Although federated learning
has proven effective in protecting privacy by avoiding data exchange between
clients and servers, it has been shown that the server can infer user ratings
based on updated non-zero gradients obtained from two consecutive rounds of
user-uploaded gradients. Moreover, federated recommendation systems (FRS) face
the challenge of heterogeneity, leading to decreased recommendation
performance. In this paper, we propose FedRec+, an ensemble framework for FRS
that enhances privacy while addressing the heterogeneity challenge. FedRec+
employs optimal subset selection based on feature similarity to generate
near-optimal virtual ratings for pseudo items, utilizing only the user's local
information. This approach reduces noise without incurring additional
communication costs. Furthermore, we utilize the Wasserstein distance to
estimate the heterogeneity and contribution of each client, and derive optimal
aggregation weights by solving a defined optimization problem. Experimental
results demonstrate the state-of-the-art performance of FedRec+ across various
reference datasets.Comment: Accepted by 59th Annual Allerton Conference on Communication,
Control, and Computin
Vertical Federated Graph Neural Network for Recommender System
Conventional recommender systems are required to train the recommendation
model using a centralized database. However, due to data privacy concerns, this
is often impractical when multi-parties are involved in recommender system
training. Federated learning appears as an excellent solution to the data
isolation and privacy problem. Recently, Graph neural network (GNN) is becoming
a promising approach for federated recommender systems. However, a key
challenge is to conduct embedding propagation while preserving the privacy of
the graph structure. Few studies have been conducted on the federated GNN-based
recommender system. Our study proposes the first vertical federated GNN-based
recommender system, called VerFedGNN. We design a framework to transmit: (i)
the summation of neighbor embeddings using random projection, and (ii)
gradients of public parameter perturbed by ternary quantization mechanism.
Empirical studies show that VerFedGNN has competitive prediction accuracy with
existing privacy preserving GNN frameworks while enhanced privacy protection
for users' interaction information.Comment: 17 pages, 9 figure
Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums
This paper proposes a new method to provide personalized tour recommendation
for museum visits. It combines an optimization of preference criteria of
visitors with an automatic extraction of artwork importance from museum
information based on Natural Language Processing using textual energy. This
project includes researchers from computer and social sciences. Some results
are obtained with numerical experiments. They show that our model clearly
improves the satisfaction of the visitor who follows the proposed tour. This
work foreshadows some interesting outcomes and applications about on-demand
personalized visit of museums in a very near future.Comment: 8 pages, 4 figures; Proceedings of the 2014 Federated Conference on
Computer Science and Information Systems pp. 439-44
A Federated Recommender System for Online Learning Environments
From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content from in-house learning management systems. The paper describes the main aspects of the federated recommender system, including its adopted architecture, the common data model used to harvest the different learning platforms, the recommendation algorithm, as well as the recommendation display widget
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